






"""simple docstring"""







import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


_UpperCamelCase	     =    logging.get_logger(__name__)  # pylint: disable=invalid-name







class    lowerCamelCase__   (			_UpperCAmelCase					):






    def __init__(					self      ,A      ,A      ,A      ,A      ,A      ,A      ,A      ,A      ,A      ,):
          super().__init__()

          if safety_checker is None:
                logger.warning(
                    F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
                    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
                    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
                    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
                    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""   )

          self.register_modules(
              speech_model=lowerCAmelCase__      ,speech_processor=lowerCAmelCase__      ,vae=lowerCAmelCase__      ,text_encoder=lowerCAmelCase__      ,tokenizer=lowerCAmelCase__      ,unet=lowerCAmelCase__      ,scheduler=lowerCAmelCase__      ,feature_extractor=lowerCAmelCase__      ,)






    def 							_UpperCamelCase				(					self      ,A = "auto"   ):


          if slice_size == "auto":
                UpperCAmelCase							     =			self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(lowerCAmelCase__   )






    def 							_UpperCamelCase				(					self   ):
          self.enable_attention_slicing(lowerCAmelCase__   )






    @torch.no_grad()
    def __call__(					self      ,A      ,A=16_000      ,A = 512      ,A = 512      ,A = 50      ,A = 7.5      ,A = None      ,A = 1      ,A = 0.0      ,A = None      ,A = None      ,A = "pil"      ,A = True      ,A = None      ,A = 1      ,**A      ,):
          UpperCAmelCase							     =			self.speech_processor.feature_extractor(
              lowerCAmelCase__      ,return_tensors="""pt"""      ,sampling_rate=lowerCAmelCase__   ).input_features.to(self.device   )
          UpperCAmelCase							     =			self.speech_model.generate(lowerCAmelCase__      ,max_length=480_000   )

          UpperCAmelCase							     =			self.speech_processor.tokenizer.batch_decode(lowerCAmelCase__      ,skip_special_tokens=lowerCAmelCase__      ,normalize=lowerCAmelCase__   )[
              0
          ]

          if isinstance(lowerCAmelCase__      ,lowerCAmelCase__   ):
                UpperCAmelCase							     =			1
          elif isinstance(lowerCAmelCase__      ,lowerCAmelCase__   ):
                UpperCAmelCase							     =			len(lowerCAmelCase__   )
          else:
                raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__   )}'''   )

          if height % 8 != 0 or width % 8 != 0:
                raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''   )

          if (callback_steps is None) or (
              callback_steps is not None and (not isinstance(lowerCAmelCase__      ,lowerCAmelCase__   ) or callback_steps <= 0)
          ):
                raise ValueError(
                    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                    F''' {type(lowerCAmelCase__   )}.'''   )

          # get prompt text embeddings
          UpperCAmelCase							     =			self.tokenizer(
              lowerCAmelCase__      ,padding="""max_length"""      ,max_length=self.tokenizer.model_max_length      ,return_tensors="""pt"""      ,)
          UpperCAmelCase							     =			text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                UpperCAmelCase							     =			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
                logger.warning(
                    """The following part of your input was truncated because CLIP can only handle sequences up to"""
                    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''   )
                UpperCAmelCase							     =			text_input_ids[:, : self.tokenizer.model_max_length]
          UpperCAmelCase							     =			self.text_encoder(text_input_ids.to(self.device   )   )[0]

          # duplicate text embeddings for each generation per prompt, using mps friendly method
          UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase							     =			text_embeddings.shape
          UpperCAmelCase							     =			text_embeddings.repeat(1      ,lowerCAmelCase__      ,1   )
          UpperCAmelCase							     =			text_embeddings.view(bs_embed * num_images_per_prompt      ,lowerCAmelCase__      ,-1   )

          # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
          # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
          # corresponds to doing no classifier free guidance.
          UpperCAmelCase							     =			guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
                UpperCAmelCase							     =			42
                if negative_prompt is None:
                      UpperCAmelCase							     =			[""""""] * batch_size
                elif type(lowerCAmelCase__   ) is not type(lowerCAmelCase__   ):
                      raise TypeError(
                          F'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase__   )} !='''
                          F''' {type(lowerCAmelCase__   )}.'''   )
                elif isinstance(lowerCAmelCase__      ,lowerCAmelCase__   ):
                      UpperCAmelCase							     =			[negative_prompt]
                elif batch_size != len(lowerCAmelCase__   ):
                      raise ValueError(
                          F'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase__   )}, but `prompt`:'''
                          F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                          """ the batch size of `prompt`."""   )
                else:
                      UpperCAmelCase							     =			negative_prompt

                UpperCAmelCase							     =			text_input_ids.shape[-1]
                UpperCAmelCase							     =			self.tokenizer(
                    lowerCAmelCase__      ,padding="""max_length"""      ,max_length=lowerCAmelCase__      ,truncation=lowerCAmelCase__      ,return_tensors="""pt"""      ,)
                UpperCAmelCase							     =			self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                UpperCAmelCase							     =			uncond_embeddings.shape[1]
                UpperCAmelCase							     =			uncond_embeddings.repeat(1      ,lowerCAmelCase__      ,1   )
                UpperCAmelCase							     =			uncond_embeddings.view(batch_size * num_images_per_prompt      ,lowerCAmelCase__      ,-1   )

                # For classifier free guidance, we need to do two forward passes.
                # Here we concatenate the unconditional and text embeddings into a single batch
                # to avoid doing two forward passes
                UpperCAmelCase							     =			torch.cat([uncond_embeddings, text_embeddings]   )

          # get the initial random noise unless the user supplied it

          # Unlike in other pipelines, latents need to be generated in the target device
          # for 1-to-1 results reproducibility with the CompVis implementation.
          # However this currently doesn't work in `mps`.
          UpperCAmelCase							     =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
          UpperCAmelCase							     =			text_embeddings.dtype
          if latents is None:
                if self.device.type == "mps":
                      # randn does not exist on mps
                      UpperCAmelCase							     =			torch.randn(lowerCAmelCase__      ,generator=lowerCAmelCase__      ,device="""cpu"""      ,dtype=lowerCAmelCase__   ).to(
                          self.device   )
                else:
                      UpperCAmelCase							     =			torch.randn(lowerCAmelCase__      ,generator=lowerCAmelCase__      ,device=self.device      ,dtype=lowerCAmelCase__   )
          else:
                if latents.shape != latents_shape:
                      raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
                UpperCAmelCase							     =			latents.to(self.device   )

          # set timesteps
          self.scheduler.set_timesteps(lowerCAmelCase__   )

          # Some schedulers like PNDM have timesteps as arrays
          # It's more optimized to move all timesteps to correct device beforehand
          UpperCAmelCase							     =			self.scheduler.timesteps.to(self.device   )

          # scale the initial noise by the standard deviation required by the scheduler
          UpperCAmelCase							     =			latents * self.scheduler.init_noise_sigma

          # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
          # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
          # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
          # and should be between [0, 1]
          UpperCAmelCase							     =			"""eta""" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
          UpperCAmelCase							     =			{}
          if accepts_eta:
                UpperCAmelCase							     =			eta

          for i, t in enumerate(self.progress_bar(lowerCAmelCase__   )   ):
                # expand the latents if we are doing classifier free guidance
                UpperCAmelCase							     =			torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
                UpperCAmelCase							     =			self.scheduler.scale_model_input(lowerCAmelCase__      ,lowerCAmelCase__   )

                # predict the noise residual
                UpperCAmelCase							     =			self.unet(lowerCAmelCase__      ,lowerCAmelCase__      ,encoder_hidden_states=lowerCAmelCase__   ).sample

                # perform guidance
                if do_classifier_free_guidance:
                      UpperCAmelCase     ,   UpperCAmelCase							     =			noise_pred.chunk(2   )
                      UpperCAmelCase							     =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                UpperCAmelCase							     =			self.scheduler.step(lowerCAmelCase__      ,lowerCAmelCase__      ,lowerCAmelCase__      ,**lowerCAmelCase__   ).prev_sample

                # call the callback, if provided
                if callback is not None and i % callback_steps == 0:
                      callback(lowerCAmelCase__      ,lowerCAmelCase__      ,lowerCAmelCase__   )

          UpperCAmelCase							     =			1 / 0.18215 * latents
          UpperCAmelCase							     =			self.vae.decode(lowerCAmelCase__   ).sample

          UpperCAmelCase							     =			(image / 2 + 0.5).clamp(0      ,1   )

          # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
          UpperCAmelCase							     =			image.cpu().permute(0      ,2      ,3      ,1   ).float().numpy()

          if output_type == "pil":
                UpperCAmelCase							     =			self.numpy_to_pil(lowerCAmelCase__   )

          if not return_dict:
                return image

          return StableDiffusionPipelineOutput(images=lowerCAmelCase__      ,nsfw_content_detected=lowerCAmelCase__   )




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a    :	List[Any]         =    logging.get_logger(__name__)

__a    :	List[Any]         =    {
    """MIT/ast-finetuned-audioset-10-10-0.4593""": (
        """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
    ),
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Optional[Any]     					=		'''audio-spectrogram-transformer'''





			def __init__(  self ,  lowerCAmelCase__=7_68 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=30_72 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=1E-12 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=1_28 ,  **lowerCAmelCase__ ,  )	->   Optional[Any]:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__		)

							__lowercase					     =				hidden_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_act
							__lowercase					     =				hidden_dropout_prob
							__lowercase					     =				attention_probs_dropout_prob
							__lowercase					     =				initializer_range
							__lowercase					     =				layer_norm_eps
							__lowercase					     =				patch_size
							__lowercase					     =				qkv_bias
							__lowercase					     =				frequency_stride
							__lowercase					     =				time_stride
							__lowercase					     =				max_length
							__lowercase					     =				num_mel_bins